Data preparation. We train our networks with small image patches sampled from the document images with a sliding window.
The basic patch size is set to 256\(\times \) 256 (suggested in [1]}).
Data augmentation is very important for boosting the performance of the neural network and we also apply augmentation methods (scale and rotation) for creating more training samples.
For scale augmentation, we sample patches with the scale factor {0.75,1.25,1.5} based on the input of the neural network and resize them to 256\(\times \) 256.
For rotation augmentation, we rotate each patch with a rotation angle 270.
Overall, more than 120,000 training patches are created for training.
